论文标题
基于出处的计划评估
Provenance-Based Assessment of Plans in Context
论文作者
论文摘要
许多现实世界的计划域都涉及各种信息源，外部实体和可变可靠性代理，所有这些都可能影响计划的信心，风险和敏感性。审查计划的人可能缺乏有关这些因素的背景；但是，此信息可在域生成期间获得，这意味着它也可以将其交织成计划者及其结果计划。本文介绍了一种基于出处的方法来解释自动计划。我们的方法（1）扩展了Shop3 HTN计划者以生成依赖性信息，（2）将依赖信息转换为已建立的PROV-O表示，（（3）使用图形传播和TMS启发的算法来支持动态和反法评估信息流，信心，信心和支持。我们鉴于自动化计划文献和信息分析文献的解释目标的解释范围，我们证明了其评估计划的相关性，敏感性，风险，假设支持，多样性和相对信心的能力。
Many real-world planning domains involve diverse information sources, external entities, and variable-reliability agents, all of which may impact the confidence, risk, and sensitivity of plans. Humans reviewing a plan may lack context about these factors; however, this information is available during the domain generation, which means it can also be interwoven into the planner and its resulting plans. This paper presents a provenance-based approach to explaining automated plans. Our approach (1) extends the SHOP3 HTN planner to generate dependency information, (2) transforms the dependency information into an established PROV-O representation, and (3) uses graph propagation and TMS-inspired algorithms to support dynamic and counter-factual assessment of information flow, confidence, and support. We qualified our approach's explanatory scope with respect to explanation targets from the automated planning literature and the information analysis literature, and we demonstrate its ability to assess a plan's pertinence, sensitivity, risk, assumption support, diversity, and relative confidence.
